code: def make_interp_spline(
      x, y, k=3, t=None, bc_type=None, axis=0, check_finite=True
  ):
      """Compute the (coefficients of) interpolating B-spline.
  
      Parameters
      ----------
      x : array_like, shape (n,)
          Abscissas.
      y : array_like, shape (n, ...)
          Ordinates.
      k : int, optional
          B-spline degree. Default is cubic, k=3.
      t : array_like, shape (nt + k + 1,), optional.
          Knots.
          The number of knots needs to agree with the number of datapoints and
          the number of derivatives at the edges. Specifically, ``nt - n`` must
          equal ``len(deriv_l) + len(deriv_r)``.
      bc_type : 2-tuple or None
          Boundary conditions.
          Default is None, which means choosing the boundary conditions
          automatically. Otherwise, it must be a length-two tuple where the first
          element sets the boundary conditions at ``x[0]`` and the second
          element sets the boundary conditions at ``x[-1]``. Each of these must
          be an iterable of pairs ``(order, value)`` which gives the values of
          derivatives of specified orders at the given edge of the interpolation
          interval.
          Alternatively, the following string aliases are recognized:
  
          * ``"clamped"``: The first derivatives at the ends are zero. This is
             equivalent to ``bc_type=([(1, 0.0)], [(1, 0.0)])``.
          * ``"natural"``: The second derivatives at ends are zero. This is
            equivalent to ``bc_type=([(2, 0.0)], [(2, 0.0)])``.
          * ``"not-a-knot"`` (default): The first and second segments are the same
            polynomial. This is equivalent to having ``bc_type=None``.
  
      axis : int, optional
          Interpolation axis. Default is 0.
      check_finite : bool, optional
          Whether to check that the input arrays contain only finite numbers.
          Disabling may give a performance gain, but may result in problems
          (crashes, non-termination) if the inputs do contain infinities or NaNs.
          Default is True.
  
      Returns
      -------
      b : a BSpline object of the degree ``k`` and with knots ``t``.
  
      Examples
      --------
  
      Use cubic interpolation on Chebyshev nodes:
  
      >>> def cheb_nodes(N):
      ...     jj = 2.*np.arange(N) + 1
      ...     x = np.cos(np.pi * jj / 2 / N)[::-1]
      ...     return x
  
      >>> x = cheb_nodes(20)
      >>> y = np.sqrt(1 - x**2)
  
      >>> from scipy.interpolate import BSpline, make_interp_spline
      >>> b = make_interp_spline(x, y)
      >>> np.allclose(b(x), y)
      True
  
      Note that the default is a cubic spline with a not-a-knot boundary condition
  
      >>> b.k
      3
  
      Here we use a 'natural' spline, with zero 2nd derivatives at edges:
  
      >>> l, r = [(2, 0.0)], [(2, 0.0)]
      >>> b_n = make_interp_spline(x, y, bc_type=(l, r))  # or, bc_type="natural"
      >>> np.allclose(b_n(x), y)
      True
      >>> x0, x1 = x[0], x[-1]
      >>> np.allclose([b_n(x0, 2), b_n(x1, 2)], [0, 0])
      True
  
      Interpolation of parametric curves is also supported. As an example, we
      compute a discretization of a snail curve in polar coordinates
  
      >>> phi = np.linspace(0, 2.*np.pi, 40)
      >>> r = 0.3 + np.cos(phi)
      >>> x, y = r*np.cos(phi), r*np.sin(phi)  # convert to Cartesian coordinates
  
      Build an interpolating curve, parameterizing it by the angle
  
      >>> from scipy.interpolate import make_interp_spline
      >>> spl = make_interp_spline(phi, np.c_[x, y])
  
      Evaluate the interpolant on a finer grid (note that we transpose the result
      to unpack it into a pair of x- and y-arrays)
  
      >>> phi_new = np.linspace(0, 2.*np.pi, 100)
      >>> x_new, y_new = spl(phi_new).T
  
      Plot the result
  
      >>> import matplotlib.pyplot as plt
      >>> plt.plot(x, y, 'o')
      >>> plt.plot(x_new, y_new, '-')
      >>> plt.show()
  
      See Also
      --------
      BSpline : base class representing the B-spline objects
      CubicSpline : a cubic spline in the polynomial basis
      make_lsq_spline : a similar factory function for spline fitting
      UnivariateSpline : a wrapper over FITPACK spline fitting routines
      splrep : a wrapper over FITPACK spline fitting routines
  
      """
      # convert string aliases for the boundary conditions
      if bc_type is None or bc_type == "not-a-knot":
          deriv_l, deriv_r = None, None
      elif isinstance(bc_type, string_types):
          deriv_l, deriv_r = bc_type, bc_type
      else:
          try:
              deriv_l, deriv_r = bc_type
          except TypeError:
              raise ValueError(
                  "Unknown boundary condition: %s" % bc_type
              )
  
      y = np.asarray(y)
  
      if not -y.ndim <= axis < y.ndim:
          raise ValueError("axis {} is out of bounds".format(axis))
      if axis < 0:
          axis += y.ndim
  
      # special-case k=0 right away
      if k == 0:
          if any(_ is not None for _ in (t, deriv_l, deriv_r)):
              raise ValueError(
                  "Too much info for k=0: t and bc_type can only "
                  "be None."
              )
          x = _as_float_array(x, check_finite)
          t = np.r_[x, x[-1]]
          c = np.asarray(y)
          c = np.rollaxis(c, axis)
          c = np.ascontiguousarray(c, dtype=_get_dtype(c.dtype))
          return BSpline.construct_fast(t, c, k, axis=axis)
  
      # special-case k=1 (e.g., Lyche and Morken, Eq.(2.16))
      if k == 1 and t is None:
          if not (deriv_l is None and deriv_r is None):
              raise ValueError(
                  "Too much info for k=1: bc_type can only be None."
              )
          x = _as_float_array(x, check_finite)
          t = np.r_[x[0], x, x[-1]]
          c = np.asarray(y)
          c = np.rollaxis(c, axis)
          c = np.ascontiguousarray(c, dtype=_get_dtype(c.dtype))
          return BSpline.construct_fast(t, c, k, axis=axis)
  
      x = _as_float_array(x, check_finite)
      y = _as_float_array(y, check_finite)
      k = operator.index(k)
  
      # come up with a sensible knot vector, if needed
      if t is None:
          if deriv_l is None and deriv_r is None:
              if k == 2:
                  # OK, it's a bit ad hoc: Greville sites + omit
                  # 2nd and 2nd-to-last points, a la not-a-knot
                  t = (x[1:] + x[:-1]) / 2.0
                  t = np.r_[
                      (x[0],) * (k + 1), t[1:-1], (x[-1],) * (k + 1)
                  ]
              else:
                  t = _not_a_knot(x, k)
          else:
              t = _augknt(x, k)
  
      t = _as_float_array(t, check_finite)
  
      y = np.rollaxis(y, axis)  # now internally interp axis is zero
  
      if x.ndim != 1 or np.any(x[1:] <= x[:-1]):
          raise ValueError("Expect x to be a 1-D sorted array_like.")
      if k < 0:
          raise ValueError("Expect non-negative k.")
      if t.ndim != 1 or np.any(t[1:] < t[:-1]):
          raise ValueError("Expect t to be a 1-D sorted array_like.")
      if x.size != y.shape[0]:
          raise ValueError("x and y are incompatible.")
      if t.size < x.size + k + 1:
          raise ValueError(
              "Got %d knots, need at least %d."
              % (t.size, x.size + k + 1)
          )
      if (x[0] < t[k]) or (x[-1] > t[-k]):
          raise ValueError("Out of bounds w/ x = %s." % x)
  
      # Here : deriv_l, r = [(nu, value), ...]
      deriv_l = _convert_string_aliases(deriv_l, y.shape[1:])
      deriv_l_ords, deriv_l_vals = _process_deriv_spec(deriv_l)
      nleft = deriv_l_ords.shape[0]
  
      deriv_r = _convert_string_aliases(deriv_r, y.shape[1:])
      deriv_r_ords, deriv_r_vals = _process_deriv_spec(deriv_r)
      nright = deriv_r_ords.shape[0]
  
      # have `n` conditions for `nt` coefficients; need nt-n derivatives
      n = x.size
      nt = t.size - k - 1
  
      if nt - n != nleft + nright:
          raise ValueError(
              "The number of derivatives at boundaries does not "
              "match: expected %s, got %s+%s" % (nt - n, nleft, nright)
          )
  
      # set up the LHS: the collocation matrix + derivatives at boundaries
      kl = ku = k
      ab = np.zeros((2 * kl + ku + 1, nt), dtype=np.float_, order="F")
      _bspl._colloc(x, t, k, ab, offset=nleft)
      if nleft > 0:
          _bspl._handle_lhs_derivatives(
              t, k, x[0], ab, kl, ku, deriv_l_ords
          )
      if nright > 0:
          _bspl._handle_lhs_derivatives(
              t, k, x[-1], ab, kl, ku, deriv_r_ords, offset=nt - nright
          )
  
      # set up the RHS: values to interpolate (+ derivative values, if any)
      extradim = prod(y.shape[1:])
      rhs = np.empty((nt, extradim), dtype=y.dtype)
      if nleft > 0:
          rhs[:nleft] = deriv_l_vals.reshape(-1, extradim)
      rhs[nleft : nt - nright] = y.reshape(-1, extradim)
      if nright > 0:
          rhs[nt - nright :] = deriv_r_vals.reshape(-1, extradim)
  
      # solve Ab @ x = rhs; this is the relevant part of linalg.solve_banded
      if check_finite:
          ab, rhs = map(np.asarray_chkfinite, (ab, rhs))
      (gbsv,) = get_lapack_funcs(("gbsv",), (ab, rhs))
      lu, piv, c, info = gbsv(
          kl, ku, ab, rhs, overwrite_ab=True, overwrite_b=True
      )
  
      if info > 0:
          raise LinAlgError("Collocation matix is singular.")
      elif info < 0:
          raise ValueError(
              "illegal value in %d-th argument of internal gbsv" % -info
          )
  
      c = np.ascontiguousarray(c.reshape((nt,) + y.shape[1:]))
      return BSpline.construct_fast(t, c, k, axis=axis)
  
imports: from __future__ import division, print_function, absolute_import
  import functools
  import operator
  import numpy
  from scipy._lib.six import string_types
  from scipy.linalg import get_lapack_funcs, LinAlgError, cholesky_banded, cho_solve_banded
  from .None import _bspl
  from .None import _fitpack_impl
  from .None import _fitpack
label_desc: Use image node_id 8 for calling a global function with example usage: make_interp_spline(x, y, k, t, bc_type, axis, check_finite) and returns: BSpline, BSpline, BSpline